Cache Poisoning in Nestjs with Cockroachdb

Cache poisoning occurs when an attacker causes cached data to be replaced with malicious or incorrect content, leading to unintended responses for subsequent users. In a NestJS application using CockroachDB, this risk can emerge from improper cache key design, insufficient input validation, or unsafe data handling patterns that allow an attacker to control what gets cached and served.

When a NestJS service queries CockroachDB with user-influenced parameters and stores the result in a cache (e.g., in-memory or distributed), weak normalization of keys or failure to validate query inputs can enable an attacker to inject crafted identifiers. For example, if an endpoint like /products/:category builds a cache key solely from the category parameter without strict validation, an attacker might provide a value such as electronics/* or include encoded user IDs, causing the same cached entry to be shared across users or contexts. CockroachDB’s SQL compatibility means typical SQL behaviors apply; without strict type checks or prepared statements, concatenated strings may yield unexpected row groupings or expose rows that should remain isolated.

Another vector arises from caching authenticated views or personalized data under a key that does not include the user or tenant context. Suppose a NestJS controller caches a database response using only the endpoint path. In that case, an authenticated user may see another user’s data if the cache key omits the user identifier, effectively leaking information across accounts. This is especially relevant in multi-tenant deployments on CockroachDB, where schema design often uses tenant IDs in rows. If the application caches a query result like SELECT * FROM orders WHERE tenant_id = $1 but the cache key excludes tenant_id, a request with a manipulated tenant ID might reuse a cached result from a different tenant, violating isolation boundaries.

SSRF and external data influence can compound cache poisoning risks. An attacker may supply a malicious payload that causes the application to fetch external data and store it in the cache under a predictable key. Subsequent legitimate requests then receive the attacker-controlled content. Because CockroachDB does not inherently validate application-level caching behavior, the vulnerability resides in how NestJS constructs queries, normalizes inputs, and forms cache keys. Unsafe consumption patterns, such as directly using raw request parameters in cache lookups or ignoring validation pipelines, amplify the problem.

To detect such issues, middleBrick scans the unauthenticated attack surface of a NestJS endpoint backed by CockroachDB, testing input handling, cache key formation, and data exposure across the 12 security checks. The scan maps findings to frameworks like OWASP API Top 10 and provides remediation guidance without attempting to fix or block anything itself.

Remediation focuses on strict input validation, deterministic and context-aware cache keys, and safe database interactions. Below are concrete examples using NestJS services with CockroachDB via a Node.js client, demonstrating secure patterns.

1. Validate and normalize inputs

Use class-validator to ensure parameters conform to expected formats before reaching the database or cache layer.

import { IsIn, validate } from 'class-validator';

export class CategoryFilter {
  @IsIn(['electronics', 'books', 'clothing', 'home'])
  category: string;
}

// In controller
const filter = new CategoryFilter();
filter.category = req.params.category;
const errors = await validate(filter);
if (errors.length > 0) {
  throw new BadRequestException('Invalid category');
}

2. Use parameterized queries with CockroachDB

Always use placeholders to prevent SQL injection and ensure consistent query shapes for caching.

import { Pool } from 'pg';

const pool = new Pool({ connectionString: process.env.COCKROACH_URL });

async function getProductsByCategory(category: string) {
  const client = await pool.connect();
  try {
    const res = await client.query('SELECT id, name FROM products WHERE category = $1', [category]);
    return res.rows;
  } finally {
    client.release();
  }
}

3. Build cache keys with full context

Include user ID, tenant ID, and validated parameters to avoid cross-user contamination.

function buildCacheKey(userId: string, tenantId: string, category: string): string {
  return `tenant:${tenantId}:user:${userId}:category:${category}`;
}

// Usage inside a service method
const key = buildCacheKey(req.user.id, req.tenant.id, filter.category);
const cached = await cache.get(key);
if (cached) {
  return cached;
}
const data = await getProductsByCategory(filter.category);
await cache.set(key, data, { ttl: 300 });
return data;

4. Isolate tenant data explicitly

Even when caching rows, ensure tenant_id is part of the query predicate and the cache key.

async function getTenantOrders(tenantId: string, userId: string) {
  const client = await pool.connect();
  try {
    const res = await client.query(
      'SELECT id, total FROM orders WHERE tenant_id = $1 AND user_id = $2',
      [tenantId, userId]
    );
    return res.rows;
  } finally {
    client.release();
  }
}

5. Avoid caching sensitive or user-specific views without scoping

If caching personalized data, incorporate tenant and user identifiers and set appropriate TTLs to limit exposure windows.

const userSpecificKey = `user:${req.user.id}:profile`;
const profile = await cache.get(userSpecificKey);
if (!profile) {
  const profileData = await getUserProfile(req.user.id);
  await cache.set(userSpecificKey, profileData, { ttl: 60 });
}
return profileData;

6. Integrate scanning into development

Use the middleBrick CLI to verify endpoint behavior and cache interactions without credentials.

middlebrick scan https://api.example.com

For CI/CD, the GitHub Action can enforce a maximum risk score before deployment, while the MCP Server lets you scan APIs directly from your IDE during development.

These steps reduce the likelihood of cache poisoning by ensuring inputs are controlled, queries are safe, and cached entries are uniquely scoped.